Dynamic panel models with small T (≤10) often face estimation challenges. Conventional OLS fixed-effects and GLS random-effects estimators produce biased results, while GMM methods are commonly used but have drawbacks. This study introduces transformed-likelihood estimators—specifically orthogonal reparameterization—as a promising alternative largely overlooked in political science research. Our findings demonstrate that this estimator significantly outperforms standard approaches like GMM when sample sizes (both T and N) are constrained. It provides better efficiency gains, especially when the lagged dependent variable coefficient is close to one, offering clearer insights into long-run effects.

Key Findings:

• The orthogonal reparameterization likelihood estimator shows superior performance for political science applications

• Provides significant efficiency gains compared to GMM in small panels

• Crucially addresses estimation issues when coefficients on lagged dependent variables are near unity

Why It Matters:

This method offers a viable solution for researchers working with limited data samples, improving accuracy and providing better estimates of long-term relationships.